Computational Fluid Dynamics as an Emerging Supporting Clinical Tool: Review on Human Airways

Objectives: The main objective of this review article is to evaluate the usability of Computational Fluid Dynamics (CFD) as a supporting clinical tool for respiratory system. Data Source: The English articles referred for this review paper were identified from various International peer reviewed journals indexed in Science citation index. Study Selection: 26 high quality articles most relevant to the highlighted topic which were published in last fifteen years were selected from almost 120 articles. Results: The analysis done and the outcome obtained by this computational method is as accurate as Spirometry and Pulmonary function test (PFT) result. CFD can be very useful in the cases where patents is unable to perform PFT. Pressure drop, Velocity profile, Wall shear stress & other flow parameter, respiratory resistance, Pattern of drug deposition, Particles transport/deposition, etc. had also been predicted accurately using CFD. The effect of tracheal stenosis on the flow parameters has been predicted. The size and location of tracheal stenosis has also been correlated with breathing difficulties. The distribution of air in various lobes of the lungs can be accurately predicted with CFD tool. Conclusion: Virtual surgery is eventually possible by using CFD after further research with validation. With the help of this multi - disciplinary and efficient tool we can obtain accurate result while reducing cost and time

Framework for progressive segmentation of chest radiograph for efficient diagnosis of inert regions

Segmentation is one of the most essential steps required to identify the inert object in the chest x-ray. A review with the existing segmentation techniques towards chest x-ray as well as other vital organs was performed. The main objective was to find whether existing system offers accuracy at the cost of recursive and complex operations. The proposed system contributes to introduce a framework that can offer a good balance between computational performance and segmentation performance. Given an input of chest x-ray, the system offers progressive search for similar image on the basis of similarity score with queried image. Region-based shape descriptor is applied for extracting the feature exclusively for identifying the lung region from the thoracic region followed by contour adjustment. The final segmentation outcome shows accurate identification followed by segmentation of apical and costophrenic region of lung. Comparative analysis proved that proposed system offers better segmentation performance in contrast to existing system